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Hindawi Publishing Corporation
EURASIP Journal on Advances in Signal Processing
Volume 2007, Article ID 57985, 5 pages
doi:10.1155/2007/57985
Research Article
Challenges and Trends in Analyses of Electric Power
Quality Measurement Data
Mark F. McGranaghan
1
and Surya Santoso
2
1
Electric Power Research Institute (EPRI Solutions), Knoxville, TN 37932, USA
2
Department of Electrical and Computer Engineering, The University of Tex as at Austin, Austin, TX 78712-0240, USA
Received 13 August 2006; Revised 13 November 2006; Accepted 13 November 2006
Recommended by Irene Y. H. Gu
Power quality monitoring has expanded from a means to investigate customer complaints to an integral part of power system
performance assessments. Besides special purpose power quality monitors, power quality data are collected from many other
monitoring devices on the system (intelligent relays, revenue meters, digital fault recorders, etc.). The result is a tremendous volume
of measurement data that is being collected continuously and must be analyzed to determine if there are important conclusions
that can b e drawn from the d ata. It is a significant challenge due to the wide range of characteristics involved, ranging from very
slow variations in the steady state voltage to microsecond transients and high frequency distortion. This paper describes some of
the problems that can be evaluated with both offline and online analyses of power quality measurement data. These applications
can dramatically increase the value of power quality monitoring systems and provide the basis for ongoing research into new
analysis and character ization methods and signal processing techniques.
Copyright © 2007 M. F. McGranaghan and S. Santoso. This is an open access article distributed under the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
1. INTRODUCTION
Electric power quality problems encompass a wide range of

different phenomena with time scales range from tens of
nanoseconds to steady state. Each of these phenomena may
have a variety of different causes and, thus, require different
solutions that can be used to improve the power quality and
equipment performance. Many power quality (PQ) prob-
lems arise from the incompatibility in the electrical environ-
ment between the utility supply system and the equipment it
serves. There are also PQ problems arising from adverse in-
teractions between the equipment and the supply system. For
instance, nonlinear loads are known to produce harmonic
currents that can excite the supply system into resonance [1].
The majority of power quality problems can be charac-
terized through measurements of voltage and current. Since
PQ disturbances are relatively infrequent and the times at
which they occur are unscheduled, continuous measurement
or monitoring over an extended period is often required.
In addition to characterizing PQ problems, PQ monitoring
has been widely used to evaluate system-wide performance
(benchmarking). By understanding the normal power qual-
ity performance of a system, a utility can identify abnormal
characteristics (may be an indication of equipment or system
problems) and can offer information to customers to help
them match their sensitive equipment characteristics with re-
alistic power quality characteristics.
Since the time scales of PQ disturbances vary widely,
power monitoring instruments should ideal ly have the capa-
bility of capturing events ranging in frequencies from DC to
a few megahertz. Many commercial power quality monitor-
ing instruments have sampling rates of 256 samples per cycle
since the majority of PQ events have frequency contents be-

low 5 kHz [1]. The availability of high-end instruments to
capture infrequent very high frequency events is limited due
to technical and economical hurdles.
As more and more PQ monitors are installed in the utility
and customer facilities, end-users of PQ monitors are often
inundated with voluminous data. It is not uncommon that
end-users undergo a “drinking from the fire hose” experience
especially at the time when the analysis results of the data
are most needed [2, 3]. The true value of any power quality
monitoring program lies in its ability to analyze and interpret
voluminous raw data, and generate actionable information
to prevent PQ problems or improve the overall power qual-
ity performance. To this end, signal processing techniques in
2 EURASIP Journal on Advances in Signal Processing
conjunction with various artificial intelligence techniques are
invaluable to meet this goal.
The objective of this paper is not to present signal pro-
cessing or ar tificial intelligent techniques, but rather to de-
scribe challenges and potential applications of signal pro-
cessing techniques in turning raw PQ measurement data to
a much more valuable commodity—knowledge and infor-
mation to improve PQ performance. Section 2 of the pa-
per presents online and offline monitoring approaches, while
Sections 3 and 4 provide descriptions on potential applica-
tions of signal processing methods to analyze raw PQ mea-
surement data. The applications described provide the basis
for research efforts (many of which are under way around the
world) to identify new and improved methods for the data
analysis and development of important conclusions from the
measurement data.

2. ONLINE AND OFFLINE POWER
QUALITY MONITORING
As utilities and industrial customers have expanded their
power quality monitoring systems, the data management,
analysis, and interpretation functions have become the most
significant challenges in the overall power quality monitoring
effort. The shift in the use of power quality monitoring sys-
tem from a traditional data acquisition system to a fully au-
tomated intelligent analysis system would tremendously in-
crease the value of power quality monitoring as proposed in
[4].
There are two streams of power quality data analysis,
that is, offline and online analyses. The offline power qual-
ity data a nalysis, as the term suggests, is performed offline
at the central processing locations. On the other hand, the
online data analysis is performed within the instrument it-
self or immediately upon collection of the information at a
central processing location. Online analysis results are very
helpful to support actions that must be taken (e.g., deter-
mination of fault location from voltage and current wave-
forms).
Offline analyses are suitable for system performance eval-
uation, problem characterization, and just-in-time mainte-
nance where rapid analysis and dissemination of analysis
results are not required. Typically offline analysis is better
suited to analyze steady-state data. Examples of signal pro-
cessing applications include the following.
(i) RMS variation analysis which includes tabulations
of voltage sags and swells, magnitude-duration scat-
ter plots based on CBEMA, ITIC, or user-specified

magnitude-duration curves, and computations of a
wide range of RMS indices such as SARFI. Signal pro-
cessing techniques can be used to quantify voltage sag
and swell perfor mance. Furthermore, signal process-
ing techniques in conjunction with the load equip-
ment models can be used to predict voltage sag im-
pacts on sensitive equipment [5, 6].
(ii) Steady state analysis which includes trends of RMS
voltages, RMS currents, negative- and zero-sequence
unbalances, real and reactive power, harmonic distor-
24 Wed.23 Tue.22 Mon.21 Sun.20 Sat.
Time
6700
6800
6900
7000
7100
7200
7300
7400
7500
V RMS A (V)
Min.[V RMS A] (V)
Avg.[V RMS A] (V)
Max.[V RMS A] (V)
SITE1-V RMS A
Figure 1: Time trend of an RMS voltage is a standard feature in
many PQ analysis software packages.
tion levels and indiv idual harmonic components, and
so forth. In addition, many software systems provide

statistical analysis of various minimum, average, maxi-
mum, standard deviation, count, cumulative probabil-
ity levels. Statistics can be temporally aggregated and
dynamically filtered. Figures 1 and 2 show the time
trend of phase A RMS voltage along with its histogram
representation. Using such steady-state data, statistical
signal processing can be used to predict performance
or the health condition of voltage regulators on distri-
bution circuits [7].
(iii) Harmonic analysis where users can calculate voltage
and current harmonic spectra, statistical analysis of
various harmonic indices, and trending over time.
Such analyses can be very useful to identify excessive
harmonic distortion on power systems as a function of
system characteristics (resonance conditions) and load
characteristics.
(iv) Transient analysis which includes statistical analysis of
maximum voltage, transient durations, and transient
frequency. These analyses can indicate switching prob-
lems with equipment such as capacitor banks.
(v) Standardized power quality reports (e.g ., daily reports,
monthly reports, statistical performance reports, exec-
utive summaries, customer PQ summaries).
(vi) Analysis of protective device operation.
(vii) Analysis of energy use.
(viii) Correlation of power quality le vels or energy use with
important parameters (e.g., voltage sag perfor mance
versus lightning flash density).
(ix) Equipment performance as a function of power quality
levels (equipment sensitivity reports).

Online power quality data assessment involves analysis
of data as they are captured. The analysis results are available
immediately for rapid dissemination. Complexity in software
design requirement for online assessment is usually higher
than that of offline. Most features available in offline analysis
M. F. McGranaghan and S. Santoso 3
7500740073007200710070006900
V RMS A (V), Avg.[V RMS A] (V)
0
5
10
15
20
25
Relative frequency (%)
0
20
40
60
80
100
Cumulative frequency (%)
Relative frequency
Cumulative frequency
SITE1 - V RMS A
Count
Min.
Avg.
4128
6871

7286
Max.
Range
St. dev.
7600
728.9
131.6
Figure 2: Histogram representation of RMS voltage indicates the
statistical distribution of the RMS voltage magnitude.
software can also be made available in a n online system. One
of the primary advantages of online data analysis is that it
can provide instant message delivery to notify users of spe-
cific events of interest. Users can then take immediate actions
upon receiving the notifications. An excellent example of an
online analysis is for locating a fault on a distribution circuit.
Signal processing techniques would be used to extract and
analyze voltage and current waveforms. The analysis would
reveal the fault location and this information would be dis-
seminated quickly to the line crew [ 8].
3. POTENTIAL FUTURE APPLICATIONS
Signal processing techniques would be very useful in devel-
oping various applications of power quality data analysis.
Some of the more import ant applications are listed in this
section. The examples described in the previous section are
also included in this listing.
3.1. Industrial power quality monitoring applications
(i) Energy and demand profiling with identification of
opportunities for energy savings and demand reduc-
tion.
(ii) Harmonics evaluations to identify transformer load-

ing concerns, sources of harmonics, problems indicat-
ing misoperation of equipment (such as converters),
and resonance concerns associated with power factor
correction.
(iii) Unbalance voltage profiling to identify impacts on
three phase motor heating and loss of life.
(iv) Voltage sag impacts evaluation to identify sensitive
equipment and possible opportunities for process ride
through improvement.
(v) Power factor correction evaluation to identify proper
operation of capacitor banks, switching concerns, res-
onance concerns, and optimizing performance to min-
imize electric bills.
(vi) Motor starting evaluation to identify switching prob-
lems, inrush current concerns, and protection device
operation.
(vii) Profiling of voltage variations (flicker) to identify load
switching and load performance problems.
(viii) Short circuit protection evaluation to evaluate proper
operation of protective devices based on short cir-
cuit current characteristics, time-current curves, and
so forth.
3.2. Power system performance assessment and
benchmarking
(i) Trending and analysis of steady-state power quality pa-
rameters (voltage regulation, unbalance, flicker, har-
monics) for performance trends, correlation with sys-
tem conditions (capacitor banks, generation, loading,
etc.), and identification of conditions that need atten-
tion.

(ii) Evaluation of steady state power quality with respect
to national and international standards. Most of these
standards involve specification of power quality per-
formance requirements in terms of statistical power
quality characteristics.
(iii) Voltage sag characterizing and assessment to identify
the cause of the voltage sags (transmission or distri-
bution) and to characterize the events for classifica-
tion and analysis (including aggregation of multiple
events and identification of subevents for analysis with
respect to protective device operations).
(iv) Capacitor switching characterizing to identify the
source of the transient (upline or downline), locate
the capacitor bank, and character ize the events for
database management and analysis.
(v) Performance indices calculation and reporting for sys-
tem benchmarking purposes and for prioritizing of
system maintenance and improvement investments.
3.3. Applications for system maintenance/
operations/reliability
(i) Locating faults. This is one of the most important ben-
efits of the monitoring systems. It can improve re-
sponse time for repairing circuits dramatically and also
identify problem conditions related to multiple faults
over time in the same location.
(ii) Capacitor bank performance assessment. Smart appli-
cations can identify fuse blowing, can failures, switch
problems (restrikes, reignitions), and resonance con-
cerns.
(iii) Voltage regulator performance assessment to identify

unusual operations, arcing problems, regulation prob-
lems, and so forth. This can be accomplished with
4 EURASIP Journal on Advances in Signal Processing
Table 1: Summary of monitoring requirements for different types of power quality variations.
Type of power quality
variation
Requirements for monitoring Analysis and display requirements
Voltage regulation
and unbalance
• 3 phase voltages
• RMS magnitudes
• Continuous monitoring with
periodic max./min./avg. samples
• Currents for response of equipment
• Trending
• Statistical evaluation of voltage
levels and unbalance levels
Harmonic distortion
• 3 phase voltages and currents
• Waveform characteristics
• 128 samples per cycle minimum
• Synchronized sampling of all voltages
and currents
• Configurable sampling characteristics
• Individual waveforms and FFTs
• Trends of harmonic levels (THD
and individual harmonics)
• Statistical characteristics of
harmonic levels
• Evaluation of neutral conductor

loading issues
• Evaluation with respect to standards
(e.g., IEEE 519, EN 50160)
• Evaluation of trends to indicate
equipment problems
Voltage sags, swells,
and short duration
interruptions
• 3 phase voltages and currents for
each event that is captured
• Configurable thresholds for
triggering events
• Characteristics of events with actual
voltage and current waveforms, as well
as RMS versus time plots
• RMS resolution of 1 cycle or better
during the RMS versus time events and
for triggering
• Waveform plots and RMS versus
time plots with pre- and post-event
information included
• Evaluation of cause of each event
(fault upline or downline from the
monitoring).
• Voltages and currents to evaluate
load interaction issues
• Magnitude duration plots
superimposed with equipment ride
through characteristics (e.g., ITIC
curve or SEMI curve)

• Statistical summary of performance
(e.g., bar charts) for benchmarking
• Evaluation of power conditioning
equipment performance during events
Trans i en t s
• 3 phase voltages and currents with
complete waveforms
• Minimum of 128 samples per cycle
for events from the power supply
system (e.g., capacitor switching)
• Configurable thresholds for
triggering
• Triggering based on waveform
variations, not just peak voltage
• Waveform plots
• Evaluation of event causes (e.g.,
capacitor switching upline or
downline from monitor)
• Correlation of events with switching
operations
• Statistical summaries of transient
performance for benchmarking
trending and associated analysis of unbalance, voltage
profiles, and voltage variations.
(iv) Distributed generator performance assessment. Smart
systems should identify interconnection issues, such
as protective device coordination problems, harmonic
injection concerns, islanding problems, and so forth.
(v) Incipient fault identifier. Research has shown that ca-
ble faults and arrester faults are often preceded by cur-

rent discharges that occur weeks before the actual fail-
ure. This is an ideal expert system application for the
monitoring system.
(vi) Transformer loading assessment can evaluate trans-
former loss of life issues related to loading and can also
include harmonic loading impacts in the calculations.
(vii) Feeder breaker performance assessment can identify
coordination problems, proper operation for short cir-
cuit conditions, nuisance tripping, and so forth.
4. SUMMARY AND FUTURE DIRECTION
Power quality monitoring is fast becoming an integr al part
of a general distribution system monitoring, as well as an
M. F. McGranaghan and S. Santoso 5
important customer service. Power producers are integrat-
ing power quality monitoring with monitoring for energy
management, evaluation of protective device operation, and
distribution automation functions. The power quality infor-
mation should be available throughout the company via the
intranet and should be m ade available to customers for eval-
uation of facility power conditioning requirements.
The power quality information should be analyzed and
summarized in a form that can be used to prioritize sys-
tem expenditures and to help customers understand the sys-
tem performance. Therefore, power quality indices should
be based on customer equipment sensitivity. The SARFI in-
dices for voltage sags are excellent examples of this con-
cept.
Power quality encompasses a wide range of conditions
and disturbances. Therefore, the requirements for the mon-
itoring system can be quite substantial, as described in this

chapter . Table 1 summarizes the basic requirements as a
function of the different types of power quality variations.
The information from power quality monitoring sys-
tems can help improve the efficiency of operating the sys-
tem and the reliability of customer operations. These are
benefits that cannot be ignored. The capabilities and appli-
cations for power quality monitors are continually evolv-
ing.
REFERENCES
[1] R.C.Dugan,M.F.McGranaghan,S.Santoso,andH.W.Beaty,
Electrical Power Systems Quality, McGraw-Hill Professional En-
gineering Series, McGraw-Hill, New York, NY, USA, 2nd edi-
tion, 2003.
[2] S. Santoso, J. Lamoree, and R. Bingham, “Answermodule: au-
tonomous exper t systems for turning raw PQ measurements
into answers,” in Proceedings of 9th International Conference on
Harmonics and Quality of Power, pp. 499–503, Orlando, Fla,
USA, October 2000.
[3] U. M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From data
mining to knowledge discovery: an overview,” in Advances
in Knowledge Discovery and Data Mining,U.M.Fayyad,G.
Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Eds., pp. 1–
34, MIT Press, Cambridge, Mass, USA, 1996.
[4]C.J.MelhornandM.F.McGranaghan,“Interpretationand
analysis of power quality measurements,” IEEE Transactions on
Industry Applications, vol. 31, no. 6, pp. 1363–1370, 1995.
[5] S.
ˇ
Z. Djoki
´

c, J. V. Milanovi
´
c, D. J. Chapman, and M. F. Mc-
Granaghan, “Shortfalls of existing methods for classification
and presentation of voltage reduction events,” IEEE Transac-
tions on Power Delivery, vol. 20, no. 2, part 2, pp. 1640–1649,
2005.
[6] S.
ˇ
Z. Djoki
´
c, J. V. Milanovi
´
c, D. J. Chapman, M. F. Mc-
Granaghan, and D. S. Kirschen, “A new method for classifica-
tion and presentation of voltage reduction events,” IEEE Trans-
actions on Power Delivery, vol. 20, no. 4, pp. 2576–2584, 2005.
[7] D. L. Brooks and D. D. Sabin, “An assessment of distribution
system power quality: volume 3: the library of distribution sys-
tem power quality monitoring case studies,” Tech. Rep. 106294,
Electric Power Research Institute, Palo Alto, Calif, USA, May
1996.
[8] S. Santoso, R. C. Dugan, J. Lamoree, and A. Sundaram, “Dis-
tance estimation technique for single line-to-ground faults in
aradial distribution system,” in IEEE of Power Engineering Soci-
ety Winter Meeting, vol. 4, pp. 2551–2555, Singapore, January
2000.
Mark F. McGranaghan is Associate Vice
President at EPRI Solutions in Knoxville,
TN, USA. He coordinates a wide range

of services offered to the electric utilities
and the critical industrial facilities through-
out the world. These services include re-
search projects, seminars, monitoring ser-
vices, power systems analysis projects, per-
formance benchmarking, testing services,
failure analysis, and designing solutions for
system performance improvement. His technical background is in
the area of power system modeling and analysis. He is an expert in
the areas of harmonic analysis, transient analysis, reliability, power
quality improvement, and power systems monitoring applications.
He has written numerous papers, is active in both IEEE and IEC
standards development, and has taught power system workshops
and seminars throughout the world.
Surya Santoso is Assistant Professor with
Department of Electrical and Computer
Engineering, The University of Texas at
Austin since 2003. He was a Senior Power
Systems/Consulting Engineer with Elec-
trotek Concepts, Knoxville, TN, between
1997 and 2003. He holds t he BSEE (1992)
degree from Satya Wacana Christian Uni-
versity, Indonesia, and the MSEE (1994)
and Ph.D. (1996) degrees from the Univer-
sity of Texas at Austin. His research interests include power sys-
tem analysis, modeling, and simulation. He is Coauthor of Electri-
cal Power Systems Quality published by McGraw-Hill, now in its
second edition. He chairs a task force on Intelligent System Ap-
plications to Data Mining and Data Analysis, and a Member of
the IEEE PES Power Systems Analysis, Computing, and Economics

Committee.